Access Behaviour Capacity and Usage on Social Media:
Exploring Higher Distance Education Students’ Perception
Djoko Rahardjo
Universitas Terbuka, Indonesia
Arifah Bintarti
Universitas Terbuka, Indonesia
Corresponding author, [email protected]
Abstract
The extensive usage of social media in today’s globalized society has had an impact on the education industry. To undertake academic and non- academic activities, social media are usually utilized by students in higher distance education to communicate with peers and instructors during the learning process. Some of them had difficulty adjusting to the new method of learning. The goal of the study is to examine how they utilize social media in relation to their access behaviour. This online survey received responses from 549 individuals in purposive samplingThe finding shows a considerable correlation between access capacity behaviour and social media usage. It is evident that access habits, which are supported by behaviour and accessibility, have a substantial effect on the use of entertainment and socialization.
Based on these findings, it is understandable that social media offer more abundantly relaxing contents that easily tempts students than the serious ones. To address this issue, educational institutions must develop several easy and engaging media-based educational tools.
Introductıon
There is no question that the rapid growth of internet connections across the globe has an impact on people’s ability to communicate in many areas such as economics, society, and culture. People also create applications to help them to make communication groups in social media. It is have new opportunities for interpersonal communication, but it is very important to have the ability to move communication to a stronger transactional level (Zubair, 2010). This creates new ways how people communicate each other. People trust social media more even though they realize that its accuracy is not guaranteed (Susanto, 2017).
Osborne-Gowey (2014) define social media (SM) as “simply a collection of websites and applications designed to build and enhance online communities for networking and sharing information.” In the same term of networking according to Boyd and Ellison (2008) Article History:
Received, 2022-06-09.
Accepted, 2023-02-15.
Keywords:
Social media usage, Social media access behaviour capacity, Higher distance education
DOI: http://dx.doi.org/10.24329/aspikom.v8i2.1161
that distinct social network sites (SNS) as facilities that permit people conducted activities as follow; they are to create a public identity inside a limited system, build a list of handlers they interact, and explore their networks and persons created by others in its system. Both terms almost the same in meaning, but for general term in this paper, although vice versa, it is maily used Social Media (SM).
The fast growth and extension of the Internet with its facilities using social-supported encompassed by means of the Internet concept have resulted in the emergence of a new field of study interest, namely SM. SM can be directly selected from data around operator interaction, activities, accomplishment like, chats, messages, emails, and so on. According to Katarzyna and Przemysław (2013), a SM is a collective of people, or more precisely, their digital accounts, joined by relationships resulting from facts about their actions, joint communication, or direct linkages acquired through Internet-based systems. While Pérez and Gómez (2011) affirmed that, online SM services like YouTube, Twitter, and Facebook are among the most popular sites throughout the world. People may utilize various tools to read and share content with friends and contacts and search for new users with similar interests. These websites have altered the way people connect on the Internet.
Some researchers in different countries have already investigated SM, for example Pornsakulvanich and Dumrongsiri (2013) investigated SM usage affected external and internal problems in Thailand. It disclosed that external and internal aspects influencing motivation and SM usage. Outside issues, mostly media, were extra affecting than internal aspects to forecasting using SM. Motivated persons in using SM for relaxation would apply more period using Facebook. Furthermore, findings indicated that females would use more time apply Twitter, YouTube, and Hi5 than men. While women inclined more to practice SM to continue their friendship than did men. While in Czech Republic, (Karlíček et al., 2012) addressed the motivation for using SM, intensity, Facebook user profiles, and user opinions about marketing actions on SM. The most significant motivation for heavy Czech Internet users to join SM is that they are always in touch with their friends.
In education context of today’s world, social networking has stimulated fresh avenues in socializing, promotion, information exchange, and learning (Eid & Al-Jabri, 2016).
Knowledge sharing and learning via SNS among college students is still unusual because social computing technology is still in its infancy, according to a study of academic literature.
Studying how social media use affects knowledge sharing and learning, researchers discovered that students who used social media to engage in online conversation, file and information sharing were more likely to succeed in their academic endeavours. SNSs have become a popular technology that was consumed in educational atmospheres (Akçayır, 2017). Even if SNS has received much academic consideration, little research has been done on students’ experiences with and expectations for the use of SNS in education. In fact, that students spend their times with social media in various reasons. The SNS usage for educational purposes was also seen well by the participants. One of the most common assumptions is that teachers will use social media to disseminate information. Although SNS usage for entertainment and relaxation is increasing, SNS use in educational matters is low, according to Guraya et al. (2019). Guraya compared the form and scope of use of SNS among medical and non-medical students. WeChat was more common among non-medical students while WhatsApp usage among medical students was significantly higher. The results showed that the level of use of SNSs was low in education by students.
WhatsApp is more common among medical students for disseminating information, and WeChat for non-medical students.
It found a correlation between the duration of teenagers employed on media and the education performance (Tartari et al., 2019). Students were found to be highly active users
of social media websites like Facebook, according to the research. The online relationships of students in social networking influence information distribution and in what way the information dividing rate influences student involvement, according to Koranteng et al.
(2019). Chinese university students’ adoption and use of SM has been studied by Liu et al. (2018) to provide metrics and recommendations to assist these students handle SM appropriately.
As stated by Norhailawati et al. (2019), when social media has grown in popularity, it has given students a style to stay in contact with their colleagues and followers while still being used in the classroom. In higher education, student retention is a concern or a real problem. It was affected that there was relation with institution’s achievement. An investigation was conducted on how students’ SM usage for academical resolutions might enhance their academics. While Valero et al. (2020) believe this in light of the growing significance of SNSs in the educational setting. The research population, educational level, and nature of the study are clearly stated on the SNS. For example, SNS is often used as a teaching tool since it provides a digital representation of the current reality.
As these numbers of students using internet rise, it is an excellent opportunity to research important aspects of SM to discover the relationships of end-user perceptions on SM usage and their access behaviour capacity. Identifying such parameters would give valuable information for enhancing present information on how students engage with applications for SM.
Social Media Usage Scale
Jenkins-Guarnieri et al. (2013) created social media usage online scale which assesses the combination of users’ social behaviours, everyday habits, and the significance of an emotional link to this use. While Shi et al. (2014) developed SM utilization form in two subscales of use and emotional occurrence. Shi defined that utilising SM was most popular pastimes, but there was not enough data to recognize the function of social media in habitual activities. The results showed that variable of usage included three aspects, while emotional experience had two fragments. While Gómez-García et al. (2020) intended to focus on developing and validating an updated questionnaire that would allow them to better understand how university students use social media. These findings backed up the creation of a solid and trustworthy questionnaire. It found points related to specific SNS were excluded, demonstrating a merging in social media activity independent of platform peculiarities. This indicated that the location was of minor significance in the situation of a new standard in which the kind of usage (watching, publishing, contributing, or engaging) trumped the network’s name.
End-user perception on SNS has been explored by Gupta and Bashir (Gupta & Bashir, 2018). Gupta and Bashir developed a scale that investigated end-user perception using a reliable and accurate questionnaire to measure social networking use, which they used to establish social networking. Academic, socialization, entertainment, and informativeness were the four elements that influenced how people used SNSs. In this study, it proposed an addition aspect to complete their work. The addition was mainly to explore how the basic habit of the user interact with SNS usage variables. The user’s habits were SNS capacity which consist of SNS accessibility, attitude toward SNS use, and access habit.
Figure 1 illustrates the purpose model of the research. This study generated the SNS Capacity construct, which comprises three dimensions: accessibility, attitude, and habit.
The final focus is on how respondents are accustomed to utilizing social networking sites. Based on Gupta and Bashir (2018), the second construct, SNS Usage, comprises four dimensions: academic, informativeness, enjoyment, and sociability. This study focuses on
academic as an endogenous latent variable because this concept was used to the educational setting.
The aim of the study is mainly to investigate and explore the way the respondents using SM. Further, the term of SM Access Behaviour Capacity (SM ABC) will be used in the study to describe how end user have their effort and be involved in using SM.
Research Question 1 (RQ1): How is SM Access Behaviour Capacity construct with the dimensions of Accessibility, Behaviour, and Habit interact each other?
Research Question 2 (RQ2): How is SM Capacity dimensions having relationship with SM Usage dimensions?
Figure 1. The Research Model
Method
Using quantitative measures, this study accessed data through the technique of purposive sampling. This study examined the use of SM by Communication Science Undergraduate Program students at Universitas Terbuka Indonesia. The research instrument was a Google Form-based online survey.
This study’s sample is comprised of Internet users from Indonesia. To ensure a coherent arrangement of questions, the instrument was evaluated using a small sample.
The link to the Google Form was shared to 1642 students across Indonesia via WhatsApp and Email Blast. There were around 578 students that returned surveys, giving a response rate of approximately 35.20 percent. Due to incomplete responses and after confirming the physical validity of the questionnaire, however, only 549 responses were utilized in the study. In this study, there were more female respondents (53,55.2%) than male respondents (46.45%), and the respondents who registered in the academic year of 2020 were 9.29% in the first semester, 45.549% in the second, 12.93% in the third, 18.03% in the fourth, and 14.42% in the fifth and higher semesters. Figure 2 depicts the geographic distribution of respondents throughout Indonesia. It demonstrates that the dispersion is widespread, however it is still uncommon to locate respondents in the eastern region of Indonesia.
Figure 2. The geographic distribution of the respondents (n=549)
The academic, informational, socialization, and entertainment elements were considered as dependent or endogenous variables in this study. Even though the three components of SM ABC, namely accessibility, behaviour, and habit, are independent or exogenous variables, Table 1 outlines the construct, its dimensions, and the research indicators. Each indicator was analysed using SmartPLS version 3, an implementation of the PLS structural equation model, using a Likert scale ranging from 1 to 5 (Ringle et al., 2015). The indicators measure the respondent’s perception of SM usage and their capacity.
Before continuing with the model analysis, the indicator loadings must be evaluated.
Table 1. Constructs, dimensions, indicators, and its loading factors
Construct Dimensions Indicators Loading Factors
SM Access Behaviour Capacity (developed in this research)
Accessibility 1.1 Network availability 0.841
1.2 Device ownership 0.877
1.3 Accessibility to SM 0.807 Access
Behaviour 2.1 Knowledge in SM 0.894
2.2 Skill using SM 0.918
2.3 Willingness accessing 0.845
Habit 3.1 Frequency using SM 0.889
3.2 Intensity managing SM 0.837
3.3 Focus in SM 0.884
Construct Dimensions Indicators Loading Factors SM Usage (adopted
from Gupta & Bashir 2018)
Academic 4.1 Academic engaging 0.773
4.2 Research referencing 0.758
4.3 Exam supporting 0.812
4.4 Discussion participating 0.845
4.5 Support Learning 0.853
4.6 Tutor Counselling 0.769
Socialization 5.1 Friendly mannering 0.827
5.2 Self Identifying 0.828
5.3 Social Meeting 0.553
5.4 Interpersonal approaching 0.797
5.5 Family Connecting 0.763
5.6 Event Updating 0.767
Entertainment 6.1 Picture Sharing 0.807
6.2 Humour Reading 0.843
6.3 Video Watching 0.843
6.4 Self refreshing 0.818
Informativeness 7.1 News Reading 0.810
7.2 Idea Sharing 0.839
7.3 Course info getting 0.834
7.4 Job Info needing 0.806
Results and Discussion
The research model assesses the psychometric qualities of the measurement model or outer model and the structural model or inner model’s variables. The analysis for the outer model focuses on validity and reliability, whereas the analysis for the inner model focuses on the significance of the influence between latent variables and the magnitude of the resulting effect.
The Measurement Model
To evaluate a measurement model, items that have low factor loadings should be removed from the analysis. According to the recommendation, loading factors above 0.708 is acceptable. This value indicates that the constructs explain more than a half of the indicator’s variance (Hair et al., 2019). While as stated by Chin et al. (2003) and rules of the thumb of Hair et al. (2011) exploratory research reliability must be a minimum of 0.60 and 0.70 or higher for confirmatory research. Table 1 also displays the loading factor of the indicators, and it is only one indicator, that is virtual social meeting, has lower than 0.708.
It is dropped for the next process.
This evaluation is also used to assess the model’s reliability and validity. Utilizing reflective indicators, the outer model’s convergent and discriminant validity from the latent constructors as well as its composite reliability were examined. Cronbach’s alpha (CA) and composite reliability (CR) must be managed to examine the constructions’ reliability.
According to Wasko and Faraj (2005), acceptable values of the CA and CR should be higher than 0.70 as the recommended value. Table 2 indicates that all CA values are more than 0.70 and CR values of each construct higher than the 0.70 threshold, which imply that internal consistency of reliability is acceptable.
The average variance extracted (AVE) shows the evaluation of construct’s convergent validity. Convergent Validity is used to prove that the statements on each latent variable in this study can be understood by the respondents in the same way as intended by the researcher. According to Hair et al. (2011) AVE of 0.50 or higher is acceptable, it has a meaning that a half or higher of the variance of the indicator can be clarified. Table 2 shows that all constructs’ AVE have upper than 0.50, it denotes that the data of the research is valid.
Table 2. Construct Reliability and Convergent validity Dimensions Cronbach’s
Alpha Composite
Reliability Average Variance
Extracted (AVE) Evaluation
Accessibility 0.795 0.880 0.709 Valid and reliable
Access behavior 0.864 0.916 0.785 Valid and reliable
Habit 0.841 0.904 0.758 Valid and reliable
Academic 0.889 0.916 0.644 Valid and reliable
Socialization 0.856 0.897 0.635 Valid and reliable
Entertainment 0.847 0.897 0.685 Valid and reliable
Informativeness 0.841 0.893 0.676 Valid and reliable
Discriminant validity is used to prove that the statements on each latent variable cannot be confused by respondents who answer the questionnaire based on statements on other latent variables, especially in terms of the meaning of the question. It is fulfilled if the AVE must be higher than the correlation involving the latent variable. Assessing the evaluation of discriminant validity is by comparing the square root of the AVE in each item with other items of the model. The sufficient discriminant validity values if the AVE root obtained for every variable is higher than the correlation of the other variables.
Discriminant validity also happened since all indicator’s loadings are greater than their cross loadings. Discriminant validity was calculated by Fornel Lacker criterion (Henseler et al., 2016). Table 3 shows that square root of AVE for the variables are upper than the other variable correlation. It indicates that discriminant validity is established.
Table 3. Discriminant validity-Fornell-Larcker Criterion
1 2 3 4 5 6 7
1. Accessibility 0.842
2. Access behaviour 0.477 0.886
3. Habit 0.431 0.677 0.871
4. Academic 0.268 0.409 0.525 0.803
5. Socialization 0.337 0.496 0.587 0.647 0.797
6. Entertainment 0.376 0.562 0.606 0.580 0.667 0.828
7. Informativeness 0.337 0.474 0.531 0.642 0.723 0.696 0.822 The Structural Model
To evaluate structural model is by investigating the estimated value of the path coefficient, i.e., the direct positive effect of latent constructs on other latent constructs as determined by bootstrapping in SmartPLS. The structural model reflects the research framework’s proposed model of path. The R2,Q2, and significant pathways are applied
to estimate the model. To get sufficient results, path coefficient among latent variables is necessity to have at least 0.1, and significant (p-value) at least 0.05 and the t-statistic value is upper than the t-table of 1.96. While the coefficient of determination (R2) has requirement to be greater than 0.19 (Mohammed et al., 2018).
The results show that all path coefficients greater than 0.1 but there are two of them having t-statistic value lower than 1.96. This shows that Habit does not influence on Informativeness with t-statistic=0.279 and Entertainment does not change on Academic with t-statistic=0.077. It means that both path coefficients are not significant. The results also display that the highest values of path coefficients are Attitude to Habit with t-statistic
= 0.610 and p-value = 0.000, and Habit to Informativeness with t-statistic=0.606 and p-value=0.000. (See Table 4)
Table 4. Path Coefficient and T-Statistic Exogenous Endogenous Path
Coefficient Standard
Deviation T
Statistics P
Values Decision Accessibility Access behavior 0.477 0.045 10.634 0.000 significant
Accessibility Habit 0.140 0.037 3.754 0.000 significant
Access behavior Habit 0.610 0.030 20.620 0.000 significant
Habit Academic 0.142 0.045 3.123 0.002 significant
Habit Socialization 0.288 0.042 6.881 0.000 significant
Habit Entertainment 0.606 0.032 19.204 0.000 significant
Habit Informativeness 0.042 0.039 1.082 0.279 not significant Socialization Academic 0.288 0.057 5.016 0.000 significant Socialization Informativeness 0.452 0.049 9.309 0.000 significant Entertainment Academic 0.102 0.057 1.769 0.077 not significant Entertainment Socialization 0.493 0.043 11.384 0.000 significant Entertainment Informativeness 0.369 0.046 8.031 0.000 significant Informativeness Academic 0.288 0.053 5.473 0.000 significant
According to Peñalver et al. (2018), the strength of every path is established by the R2 value for the dependent variable. İn other words, the observational value of R2 for the endogenous latent variable indicates that the variance in the latent variable is clarified by exogenous variable. The strength value according to Falk and Miller (1992), R2 equal or greater than 0.10 means adequate. While Chin (1998) described that R2 value greater than 0.67 indicates strong, R2 value of less than 0.33 indicates moderate, and R2 value of less than 0.19 indicates weak. The results shows that all R2 values are greater than 0.1 so then, the ability to forecast has been established. It also shows that all R2 values have moderate strength, and the highest values are Academic and Informativeness which have R2 of 0.505 and 0.606 (See Table 5).
Furthermore, Q2 creates the predictive relevance of endogenous latent variables. The values of Q2 greater than 0.0 specifies that the model is predictive relevance. Table 5 also shows that all endogenous constructs in this study have Q2 values higher than 0.0. It means that all endogenous latent variables of the model are predictive relevance.
Table 5. R-Square and Q-Square
Endogenous
Construct R2 Original T -
Statistics p Values Strength Q² Predictive
Access behaviour 0.227 5.342 0.000 moderate 0.179 relevance
Habit 0.474 13.633 0.000 moderate 0.352 relevance
Academic 0.505 14.874 0.000 moderate 0.316 relevance
Socialization 0.498 12.358 0.000 moderate 0.308 relevance
Entertainment 0.367 9.633 0.000 moderate 0.249 relevance
Informativeness 0.606 18.700 0.000 moderate 0.404 relevance
Model Fit and Final Model Evaluation
The model fit was weighed using SRMR. The SRMR value below 0.10, representing acceptable model fit (Hair et al., 2019). The findings suggest that there is relevance in the constructs’ prediction. SRMR values of the estimated model in this study is 0.071, which is less than the required of 0.10, suggesting satisfactory model fit.
Figure 3. The Final Model with Path Coefficients and T-Statistics
To answer RQ1, Figure 3 displays the final model of the research. The interaction of SM ABC’s dimensions can be described as follows; Habit variable becomes the focus of the dimensions which is supported by accessibility and attitude. Accesibility variable supports weakly to habit but also it supports indirectly via attitude variable.
While in answering RQ2, the interactions of the whole model, between SM ABC and SM Usage, it found that the main important role is that habit variable has strongest
relationship with entertainment variable of SM Usage dimensions and continuing with indirectly has strong enough connection with Socialization variable. Habit variable has a weak relationship with academic variable, but it is connected through entertainment variable and then socialization variable moderately.
Conclusion and Recommendation
This study is to investigate the impact of SNS capacity on students’ SNS usage. In general, the SM ABC construct and the accessibility component support the respondent’s attitude and habit somewhat and slightly, respectively. While the attitude element modestly supports the habit factor, the SM ABC construct supports each other adequately in its own dimension.
The SM usage patterns of respondents correlate somewhat with the entertainment component, which correlates moderately with the socialization and informativeness elements. While SM usage has a weakest link with academic and the socialization component, this variable failed to correlate with informativeness.
With the increase of SM ABC, the entertainment aspect of SM usage will enhance.
In an educational setting, video viewing will serve as a conduit for student learning. In addition to entertainment channels, YouTube features numerous educational channels.
The students will utilize them with a sense of enjoyment. It is in line with Pornsakulvanich and Dumrongsiri (2013), who discovered that participants accessed SM for relaxation more period. The reality shows that the large number of internet users in Indonesia, as well as the high frequency of accessing information content and social media, do not necessarily guarantee the ‘maturity’ of Indonesian users in using the Internet (Sulthan & Istiyanto, 2019). While Karlíček et al. (2012) found that SM change how people browse the Internet and the use of SM more likely to socialize with their colleagues.
In contrast, entertainment adequately influenced socializing and informativeness. It indicates that through gaining access to entertainment, students receive a wide variety of experiences from which they can obtain knowledge and share it with others. In accordance with Pérez and Gómez (2011), who discovered that people with similar interests will share any knowledge, but contradictory to Eid and Al-Jabri (2016), who discovered that knowledge sharing did not occur in an educational context. This is because of media technology was still a new method to the student at that time.
How to improve academic and informational quality is the implication for student institutions. Both elements are crucial to the educational situation. In fact, given the importance of entertainment to students, the institution should supply sufficient informal knowledge through the media to provide students with other entry points. To boost the use of SM, it is suggested that the access device and the internet network be upgraded. It depends not only on the students themselves, but also on government development plans aimed at expanding the nation’s internet infrastructure and services.
Acknowledgements
I would like to express my deepest gratitude to Ojat Darojat, M. Bus, PhD, Rector of Universitas Terbuka, Indonesia for the valuable financial support for this research. Without his decision, the research would not be carried out anyway.
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